Dynamics of Cortical Degeneration Over a Decade in Huntington’s Disease

Abstract

Background Characterizing changing brain structure in neurodegeneration is fundamental to understanding long-term effects of pathology and ultimately providing therapeutic targets. It is well established that Huntington’s disease (HD) gene carriers undergo progressive brain changes during the course of disease, yet the long-term trajectory of cortical atrophy is not well defined. Given that genetic therapies currently tested in HD are primarily expected to target the cortex, understanding atrophy across this region is essential. Methods Capitalizing on a unique longitudinal dataset with a minimum of 3 and maximum of 7 brain scans from 49 HD gene carriers and 49 age-matched control subjects, we implemented a novel dynamical systems approach to infer patterns of regional neurodegeneration over 10 years. We use Bayesian hierarchical modeling to map participant- and group-level trajectories of atrophy spatially and temporally, additionally relating atrophy to the genetic marker of HD (CAG-repeat length) and motor and cognitive symptoms. Results We show, for the first time, that neurodegenerative changes exhibit complex temporal dynamics with substantial regional variation around the point of clinical diagnosis. Although widespread group differences were seen across the cortex, the occipital and parietal regions undergo the greatest rate of cortical atrophy. We have established links between atrophy and genetic markers of HD while demonstrating that specific cortical changes predict decline in motor and cognitive performance. Conclusions HD gene carriers display regional variability in the spatial pattern of cortical atrophy, which relates to genetic factors and motor and cognitive symptoms. Our findings indicate a complex pattern of neuronal loss, which enables greater characterization of HD progression.

Volumetric MRI-Based Biomarkers in Huntington's Disease: An Evidentiary Review

“…Overall, the evidence from observational studies relating change in clinical outcomes to change in vMRI metrics appears sparser than the cross-sectional relationships described earlier, with the strongest longitudinal relationships relating rates of brain volume loss to changes in cognitive and composite measures rather than purely motor measures ( 2 , 19 , 20 , 64 , 66 ) ( Figure 6 ). For example, these studies have revealed relatively weak longitudinal associations between change in vMRI and change in UHDRS-TMS over a typical clinical trial time frame (1–2 years), despite the presence of a significant cross-sectional relationship between them.…”

“…Voxel-wise analyses have also identified increasing losses of gray matter volume in the occipital, parietal, frontal and insular cortices as the disease advances ( 2 , 63 ). A recent dynamic modeling approach that used 6 years of TRACK-HD and Track-On HD data revealed a temporal sequence of regional changes around the time of clinical motor diagnosis that involves not only the caudate and putamen but also cortical regions, including sensorimotor areas ( 64 ).…”

Huntington disease: new insights into molecular pathogenesis and therapeutic opportunities

“…The mutant huntingtin protein (mHTT) that results from CAG repeat expansion affects many cellular functions, leading to cell death, and establishing which of these effects are primary or secondary pathogenic processes is difficult. Striatal medium spiny neurons are most vulnerable to the presence of mHTT, although substantial neuronal dysfunction and death also occurs in the cerebral cortex [14][15][16][17][18] .…”

Planning deficits in Huntington’s disease: A brain structural correlation by voxel-based morphometry

“…Usually, cognitive impairments exhibited in HD patients are associated to caudate nucleus decrease due to the spiny neurons atrophy [ 31 ]. This decrease seems to affect particularly the caudate nucleus during the clinical debut, specially compromising the prefrontal cortico-striatal loop [ 2 , 32 , 33 ]. However, our study found that putamen deterioration in HD patients also contributes to the accuracy decrease to solve planning problems.…”